SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 641–650 of 712 papers

TitleStatusHype
Graph-Aware Transformer: Is Attention All Graphs Need?—0
SHADOWCAST: Controllable Graph Generation—0
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative ModelsCode0
Visual Relationship Detection using Scene Graphs: A Survey—0
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations—0
R2RML and RML Comparison for RDF Generation, their Rules Validation and Inconsistency Resolution—0
TOMA: Topological Map Abstraction for Reinforcement Learning—0
Molecular Inverse-Design Platform for Material Industries—0
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets—0
Graph Deconvolutional Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified